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Automatic Detection of Epileptic Waves in Electroencephalograms Using Bag of Visual Words and Machine Learning

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 12241)

Abstract

Epilepsy is one of the most recurrent brain disorders worldwide and mainly affects children. As a diagnostic support, the electroencephalogram is used, which is relatively easy to apply but requires a long time to analyze. Automatic EEG analysis presents difficulties both in the construction of the database and in the extracted characteristics used to build models. This article a machine learning-based methodology that uses a visual word bag of raw EEG images as input to identify images with abnormal signals. The performance introduces of the algorithms was tested using a proprietary pediatric EEG database. Accuracy greater than 95% was achieved, with calculation times less than 0.01 s per image. Therefore, the paper demonstrates the feasibility of using machine learning algorithms to directly analyze EEG images.

Keywords

  • Childhood epilepsy
  • Feature extraction and selection
  • Supervised classification
  • Visual categorization
  • Semantic categorization

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Acknowledgment

This work was funded by the Colombian Agency for Science, Technology, and Innovation - COLCIENCIAS - in call 715-2015, “Call for Research and Development Projects in Engineering” Project “NeuroMoTIC: Mobile System for Diagnostic Support of Epilepsy,” contract number FP44842-154-2016.

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Correspondence to Diego M. López .

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Muñoz, M.S., Torres, C.E.S., López, D.M., Salazar-Cabrera, R., Vargas-Cañas, R. (2020). Automatic Detection of Epileptic Waves in Electroencephalograms Using Bag of Visual Words and Machine Learning. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_15

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  • DOI: https://doi.org/10.1007/978-3-030-59277-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59276-9

  • Online ISBN: 978-3-030-59277-6

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